DBMS > Faircom EDGE vs. IRONdb vs. MarkLogic vs. TimesTen vs. XTDB

System Properties Comparison Faircom EDGE vs. IRONdb vs. MarkLogic vs. TimesTen vs. XTDB

Editorial information provided by DB-Engines

Name

IRONdb seems to be discontinued. Therefore it is excluded from the DB-Engines Ranking.

Description

FairCom EDGE is an Industry 4.0 solution built to integrate, collect, aggregate and synchronize mission-critical data in edge computing environments

A distributed Time Series DBMS with a focus on scalability, fault tolerance and operational simplicity

Operational and transactional Enterprise NoSQL database

In-Memory RDBMS compatible to Oracle

A general purpose database with bitemporal SQL and Datalog and graph queries

Primary database model

Key-value store
Relational DBMS

Time Series DBMS

Document store
Native XML DBMS
RDF store

as of version 7
Search engine

Relational DBMS

Document store

DB-Engines Ranking measures the popularity of database management systems
Trend Chart

Score	0.02
Rank	#368	Overall
	#54	Key-value stores
	#156	Relational DBMS

Score	5.92
Rank	#58	Overall
	#10	Document stores
	#1	Native XML DBMS
	#1	RDF stores
	#6	Search engines

Score	1.31
Rank	#163	Overall
	#74	Relational DBMS

Score	0.11
Rank	#343	Overall
	#46	Document stores

Website

www.faircom.com/products/faircom-edge

www.circonus.com/solutions/time-series-database/

www.marklogic.com

www.oracle.com/database/technologies/related/timesten.html

github.com/xtdb/xtdb
www.xtdb.com

Technical documentation

docs.faircom.com/docs/en/UUID-23d4f1fd-d213-f6d5-b92e-9b7475baa14e.html

docs.circonus.com/irondb/category/getting-started

docs.marklogic.com

docs.oracle.com/database/timesten-18.1

www.xtdb.com/docs

Developer

FairCom Corporation

Circonus LLC.

MarkLogic Corp.

Oracle, TimesTen Performance Software, HP

originally founded in HP Labs it was acquired by Oracle in 2005

Juxt Ltd.

Initial release

1979

2017

2001

1998

2019

Current release

V3, October 2020

V0.10.20, January 2018

11.0, December 2022

11 Release 2 (11.2.2.8.0)

1.19, September 2021

License

Commercial or Open Source

commercial

Restricted, free version available

commercial

restricted free version is available

commercial

Open Source

MIT License

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

Providers of DBaaS offerings, please contact us to be listed.

Implementation language

ANSI C, C++

C and C++

C++

Clojure

Server operating systems

Android
Linux

ARM, x86
Raspbian
Windows

Linux

Linux
OS X
Windows

AIX
HP-UX
Linux
OS X
Solaris SPARC/x86
Windows

All OS with a Java 8 (and higher) VM
Linux

Data scheme

Flexible Schema (defined schema, partial schema, schema free)

schema-free

Schema can be enforced

yes

schema-free

Typing

predefined data types such as float or date

yes, ANSI Standard SQL Types

yes

text, numeric, histograms

yes

yes, extensible-data-notation format

XML support

Some form of processing data in XML format, e.g. support for XML data structures, and/or support for XPath, XQuery or XSLT.

yes

Secondary indexes

yes

SQL

Support of SQL

yes

ANSI SQL queries

SQL-like query language (Circonus Analytics Query Language: CAQL)

yes

SQL92

yes

limited SQL, making use of Apache Calcite

APIs and other access methods

ADO.NET
Direct SQL
IoT Microservice layer
JDBC
MQTT (Message Queue Telemetry Transport)
ODBC
RESTful HTTP API

HTTP API

Java API
Node.js Client API
ODBC
proprietary Optic API

Proprietary Query API, introduced with version 9
RESTful HTTP API
SPARQL
WebDAV
XDBC
XQuery
XSLT

JDBC
ODBC
ODP.NET
Oracle Call Interface (OCI)

HTTP REST
JDBC

Supported programming languages

C
C#
C++
Java
JavaScript
PHP
Python
VB.Net

.Net
C
C++
Clojure
Erlang
Go
Haskell
Java
JavaScript
JavaScript (Node.js)
Lisp
Lua
Perl
PHP
Python
R
Ruby
Rust
Scala

C
C#
C++
Java
JavaScript (Node.js)
Perl
PHP
Python
Ruby

C
C++
Java
PL/SQL

Clojure
Java

Server-side scripts

Stored procedures

yes

.Net, JavaScript, C/C++

yes, in Lua

yes

via XQuery or JavaScript

PL/SQL

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

File partitioning

Customizable business rules for partitioning

Automatic, metric affinity per node

Sharding

none

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Synchronous and asynchronous realtime replication based on transaction logs

configurable replication factor, datacenter aware

yes

Multi-source replication
Source-replica replication

yes, each node contains all data

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

via Hadoop Connector, HDFS Direct Access and in-database MapReduce jobs

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency
Tunable Consistency

Immediate consistency per node, eventual consistency across nodes

Immediate Consistency

Immediate Consistency or Eventual Consistency depending on configuration

Foreign keys

Referential integrity

yes

when using SQL

yes

Transaction concepts

Support to ensure data integrity after non-atomic manipulations of data

ACID

can act as a resource manager in an XA/JTA transaction

ACID

Concurrency

Support for concurrent manipulation of data

yes

across SQL and NoSQL

yes

Durability

Support for making data persistent

yes

by means of logfiles and checkpoints

yes, flexibel persistency by using storage technologies like Apache Kafka, RocksDB or LMDB

In-memory capabilities

Is there an option to define some or all structures to be held in-memory only.

yes

yes, with Range Indexes

yes

User concepts

Access control

Fine grained user, group and file access rights managed across SQL (per ANSI standard) and NoSQL.

Role-based access control at the document and subdocument levels

fine grained access rights according to SQL-standard

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